Designing Reliable Cathode System for High-Performance Inorganic Solid-State Pouch Cells.

All-solid-state batteries (ASSBs) based on inorganic solid electrolytes fascinate a large body of researchers in terms of overcoming the inferior energy density and safety issues of existing lithium-ion batteries. To date, the cathode designs in the ASSBs achieve remarkable achievements, adding the urgency of scaling up the battery system toward inorganic solid-state pouch cell configuration for the application market. Herein, the recent developments of cathode materials and the design considerations for their application in the pouch cell format are reviewed to straighten out the roadmap of ASSBs. Specifically, the intercalation compounds and the conversion materials with conversion chemistries are highlighted and discussed as two potentially valuable material types. This review focuses on the basic electrochemical mechanisms, mechanical contact issues, and sheet-type structure in inorganic solid-state pouch cells with corresponding perspectives, thus guiding the future research direction. Finally, the benchmarks for manufacturing inorganic solid-state pouch cells to meet practical high energy density targets are provided in this review for the development of commercially viable products.

The discrepancy of NH3 oxidation mechanism between SAPO-34 and Cu/SAPO-34.

The difference of NH3 oxidation mechanism over SAPO-34 and Cu-SAPO-34 was studied. XRD (X-ray diffraction), SEM (scanning electron microscopy) and H2-TPR (H2-temperature programmed desorption) were conducted to estimate the Cu species distribution. The quantity of individual Cu2+ ions escalated with the elevation of silicon content in the Cu/SAPO-34 catalysts, leading to an enhancement in the activity of the NH3-SCR (ammonia-selective catalytic reduction) process. This augmentation in activity can be attributed to the increased presence of isolated Cu2+ species, which are pivotal in facilitating the catalytic reaction. In addition, the kinetic test of NH3 oxidation indicated that the CuO species were the active sites for NH3 oxidation. Specifically, the strong structural Brønsted acid sites were the NH3 oxidation active sites over the SAPO-34 support, and the NH3 reacted with the O2 on the Brønsted acid sites to produce the NO mainly. While the NH3 oxidation mechanism over Cu/SAPO-34 consisted of two steps: firstly, NH3 reacted with O2 on CuO sites or residual Brønsted acid sites to form NO as the product; subsequently, the generated NO was reduced by NH3 into N2 on isolated Cu2+ sites. Simultaneously, the isolated Cu2+ sites might demonstrate a significant function in the NH3 oxidation process to form N2. The identification of active sites and corresponding mechanism could deepen the understanding of excellent performance of NH3-SCR over the Cu/SAPO-34 catalyst at high temperature.

Advances in methane emissions from agricultural sources: Part I. Accounting and mitigation.

Methane is one of the major greenhouse gases (GHGs) and agriculture is recognized as its primary emitter. Methane accounting is a prerequisite for developing effective agriculture mitigation strategies. In this review, methane accounting methods and research status for various agricultural emission source including rice fields, animal enteric fermentation and livestock and poultry manure management were overview, and the influencing factors of each emission source were analyzed and discussed. At the same time, it analyzes the different research efforts involving agricultural methane accounting and makes recommendations based on the actual situation. Finally, mitigation strategies based on accounting results and actual situation are proposed. This review aims to provide basic data and reference for agriculture-oriented countries and regions to actively participate in climate action and carry out effective methane emission mitigation.

Enhanced Electrocatalytic Nitrate Reduction to Ammonia Using Functionalized Multi-Walled Carbon Nanotube-Supported Cobalt Catalyst.

Ammonia (NH3) is vital in modern agriculture and industry as a potential energy carrier. The electrocatalytic reduction of nitrate (NO3-) to ammonia under ambient conditions offers a sustainable alternative to the energy-intensive Haber-Bosch process. However, achieving high selectivity in this conversion poses significant challenges due to the multi-step electron and proton transfer processes and the low proton adsorption capacity of transition metal electrocatalysts. Herein, we introduce a novel approach by employing functionalized multi-walled carbon nanotubes (MWCNTs) as carriers for active cobalt catalysts. The exceptional conductivity of MWCNTs significantly reduces charge transfer resistance. Their unique hollow structure increases the electrochemical active surface area of the electrocatalyst. Additionally, the one-dimensional hollow tube structure and graphite-like layers within MWCNTs enhance adsorption properties, thus mitigating the diffusion of intermediate and stabilizing active cobalt species during nitrate reduction reaction (NitRR). Using the MWCNT-supported cobalt catalyst, we achieved a notable NH3 yield rate of 4.03 mg h-1 cm-2 and a high Faradaic efficiency of 84.72% in 0.1 M KOH with 0.1 M NO3-. This study demonstrates the potential of MWCNTs as advanced carriers in constructing electrocatalysts for efficient nitrate reduction.

Multiple hydrogen bonding driven supramolecular architectures and their biomedical applications.

Supramolecular chemistry combines the strength of molecular assembly via various molecular interactions. Hydrogen bonding facilitated self-assembly with the advantages of directionality, specificity, reversibility, and strength is a promising approach for constructing advanced supramolecules. There are still some challenges in hydrogen bonding based supramolecular polymers, such as complexity originating from tautomerism of the molecular building modules, the assembly process, and structure versatility of building blocks. In this review, examples are selected to give insights into multiple hydrogen bonding driven emerging supramolecular architectures. We focus on chiral supramolecular assemblies, multiple hydrogen bonding modules as stimuli responsive sources, interpenetrating polymer networks, multiple hydrogen bonding assisted organic frameworks, supramolecular adhesives, energy dissipators, and quantitative analysis of nano-adhesion. The applications in biomedical materials are focused with detailed examples including drug design evolution for myotonic dystrophy, molecular assembly for advanced drug delivery, an indicator displacement strategy for DNA detection, tissue engineering, and self-assembly complexes as gene delivery vectors for gene transfection. In addition, insights into the current challenges and future perspectives of this field to propel the development of multiple hydrogen bonding facilitated supramolecular materials are proposed.

The interface-mediated electron structure tuning of RuOx-Co3O4 nano-particles for efficient electrocatalytic nitrate reduction.

The energy-intensive processes for the industrial production of ammonia necessitates the development of new methods to be proposed that will aid in reducing the global energy consumption. Specifically, the electrocatalytic nitrate reduction reaction (NO3RR) to produce ammonia is more thermodynamically feasible than the electrocatalytic nitrogen reduction reaction (NRR). However, it is hindered by a low catalytic activity due to its complex reaction pathways. Herein, we synthesized a novel electrocatalyst, RuOx-Co3O4 nanoparticles, with abundant interfaces, which exhibited an enhanced catalytic activity for efficient ammonia synthesis. This catalyst delivered a partial current density of 65.8 mA cm-2 for NH3 production, a faradaic efficiency (FE) of 89.7%, and a superior ammonia yield rate of up to 210.5 µmol h-1 cm-2 at -0.6 V vs. RHE. X-ray photoelectron and Raman spectroscopy revealed that the formed interfacial Ru-O-Co bond can decorate the electronic structures of the active sites and accelerate the absorption of NO3-, thus promoting the production of ammonia.

Synthesis and Properties of Cefixime Core-Shell Magnetic Nano-Molecularly Imprinted Materials.

Novel core-shell magnetic molecularly imprinted polymers (MMIPs) were synthesized using the sol-gel method for the adsorption of cefixime (CFX). Fe3O4@SiO2 is the core, and molecularly imprinted polymers (MIPs) are the shell, which can selectively interact with CFX. The preparation conditions, adsorption kinetics, adsorption isotherms, selective adsorption ability, and reutilization performance of the MMIPs were investigated. The adsorption capacity of MMIPs for CFX was 111.38 mg/g, which was about 3.5 times that of MNIPs. The adsorption equilibrium time was 180 min. The dynamic adsorption experiments showed that the adsorption process of MMIPs to CFX conformed to the pseudo-second-order model. Through static adsorption study, the Scatchard analysis showed that MMIPs had two types of binding sites-the high-affinity binding sites and the low-affinity binding sites-while the Langmuir model fit the adsorption isotherms well (R2 = 0.9962). Cefepime and ceftiofur were selected as the structural analogs of CFX for selective adsorption studies; the adsorption of CFX by MMIPs was higher than that of other structural analogs; and the imprinting factors of CFX, cefepime, and ceftiofur were 3.5, 1.7, and 1.4, respectively. Furthermore, the MMIPs also showed excellent reusable performance.

Robust superhydrophobic silicone/epoxy functional coating with excellent chemical stability and self-cleaning ability.

Superhydrophobic surfaces have attracted broad attention because of their unique water repellency but are restricted by poor wear resistance, weak adhesion to the substrate, and complex fabrication processes. Herein, a double-layer coating strategy consisting of the amino fluorine-silicone resin/epoxy resin (AFSR/EP) system is created. The system features a high hardness and transparent hydrophobic interface adhesive layer through the amine-epoxy "click" chemical reaction. The environmentally friendly resin system and low-cost nano-silica particles (n-SiO2) are composited and sprayed onto the substrate surface to form a superhydrophobic layer with outstanding robustness and excellent environmental stability. The prepared AFSR/EP@n-SiO2 composite coatings have a water contact angle of 161.1° and a sliding angle of 3.4°, demonstrating high superhydrophobic properties. Benefitting from the complementary advantages of silicone/epoxy resin, the prepared composite coatings maintain remarkable water repellency after various harsh environmental tests, including cyclic mechanical abrasion and tape-stripping, acid-base (pH 1 and pH 14) treatment, 10 wt% NaCl (pH 7) salt solution immersion, temperature treatment, knife scratching, and long-term ultraviolet radiation treatment, showing reinforced mechanical robustness and durable anti-corrosion stability. Notably, surface hardness of 5H and optical transparency over 80% can be achieved. The simple method offers a novel approach for the large-scale preparation of multifunctional superhydrophobic coatings.

Synergistic Effect of Dual-Doped Carbon on Mo2C Nanocrystals Facilitates Alkaline Hydrogen Evolution.

Molybdenum carbide (Mo2C) materials are promising electrocatalysts with potential applications in hydrogen evolution reaction (HER) due to low cost and Pt-like electronic structures. Nevertheless, their HER activity is usually hindered by the strong hydrogen binding energy. Moreover, the lack of water-cleaving sites makes it difficult for the catalysts to work in alkaline solutions. Here, we designed and synthesized a B and N dual-doped carbon layer that encapsulated on Mo2C nanocrystals (Mo2C@BNC) for accelerating HER under alkaline condition. The electronic interactions between the Mo2C nanocrystals and the multiple-doped carbon layer endow a near-zero H adsorption Gibbs free energy on the defective C atoms over the carbon shell. Meanwhile, the introduced B atoms afford optimal H2O adsorption sites for the water-cleaving step. Accordingly, the dual-doped Mo2C catalyst with synergistic effect of non-metal sites delivers superior HER performances of a low overpotential (99 mV@10 mA cm-2) and a small Tafel slope (58.1 mV dec-1) in 1 M KOH solution. Furthermore, it presents a remarkable activity that outperforming the commercial 10% Pt/C catalyst at large current density, demonstrating its applicability in industrial water splitting. This study provides a reasonable design strategy towards noble-metal-free HER catalysts with high activity.

Advanced Materials Design for Adsorption of Toxic Substances in Cigarette Smoke.

Cigarettes, despite being economically important legal consumer products, are highly addictive and harmful, particularly to the respiratory system. Tobacco smoke is a complex mixture containing over 7000 chemical compounds, 86 of which are identified to have "sufficient evidence of carcinogenicity" in either animal or human tests. Thus, tobacco smoke poses a significant health risk to humans. This article focuses on materials that help reduce the levels of major carcinogens in cigarette smoke; these include nicotine, polycyclic aromatic hydrocarbons, tobacco-specific nitrosamines, hydrogen cyanide, carbon monoxide, and formaldehyde. Specifically, the research progress on adsorption effects and mechanisms of advanced materials such as cellulose, zeolite, activated carbon, graphene, and molecularly imprinted polymers are highlighted. The future trends and prospects in this field are also discussed. Notably, with advancements in supramolecular chemistry and materials engineering, the design of functionally oriented materials has become increasingly multidisciplinary. Certainly, several advanced materials can play a critical role in reducing the harmful effects of cigarette smoke. This review aims to serve as an insightful reference for the design of hybrid and functionally oriented advanced materials.

Inorganic Skeleton Reinforcement-A Generic Approach to Improve the Mechanical Properties of Biochar.

Biochar is considered as a promising candidate for emerging sustainable energy systems and environmental technology applications. However, the improvement of mechanical properties remains challenges. Herein, we propose a generic strategy to enhance the mechanical properties of bio-based carbon materials through inorganic skeleton reinforcement. As a proof-of-concept, silane, geopolymer, and inorganic gel are selected as precursors. The composites' structures are characterized and an inorganic skeleton reinforcement mechanism is elucidated. Specifically, two types of reinforcement of the silicon-oxygen skeleton network formed in situ with biomass pyrolysis and the silica-oxy-al-oxy network are constructed to improve the mechanical properties. A significant improvement in mechanical strength was achieved for bio-based carbon materials. The compressive strength of well-balanced porous carbon materials modified by silane can reach up to 88.9 kPa, geopolymer-modified carbon material exhibits an enhanced compressive strength of 36.8 kPa, and that of inorganic-gel-polymer-modified carbon material is 124.6 kPa. Moreover, the prepared carbon materials with enhanced mechanical properties show excellent adsorption performance and high reusability for organic pollutant model compound methylene blue dye. This work demonstrates a promising and universal strategy for enhancing the mechanical properties of biomass-derived porous carbon materials.

Design and Synthesis of Functional Silane-Based Silicone Resin and Application in Low-Temperature Curing Silver Conductive Inks.

In the field of flexible electronics manufacturing, inkjet printing technology is a research hotspot, and it is key to developing low-temperature curing conductive inks that meet printing requirements and have suitable functions. Herein, methylphenylamino silicon oil (N75) and epoxy-modified silicon oil (SE35) were successfully synthesized through functional silicon monomers, and they were used to prepare silicone resin 1030H with nano SiO2. 1030H silicone resin was used as the resin binder for silver conductive ink. The silver conductive ink we prepared with 1030H has good dispersion performance with a particle size of 50-100 nm, as well as good storage stability and excellent adhesion. Additionally, the printing performance and conductivity of the silver conductive ink prepared with n,n-dimethylformamide (DMF): proprylene glycol monomethyl ether (PM) (1:1) as solvent are better than those of the silver conductive ink prepared by DMF and PM solvent. Cured at a low temperature of 160 °C, the resistivity of 1030H-Ag-82%-3 conductive ink is 6.87 × 10-6 Ω·m, and that of 1030H-Ag-92%-3 conductive ink is 0.564 × 10-6 Ω·m, so the low-temperature curing silver conductive ink has high conductivity. The low-temperature curing silver conductive ink we prepared meets the printing requirements and has potential for practical applications.

An integrated method with adaptive decomposition and machine learning for renewable energy power generation forecasting.

In recent years, traditional energy sources have caused a variety of negative impacts on the environment, and reducing carbon emissions is a top priority. The development of renewable energy technology is the key to transform the energy structure. Renewable energy represented by wind energy and photovoltaics has abundant reserves so they are connected to the grid system on a large scale. However, because of natural energy's randomness, renewable energy power generation poses potential risks to energy production and grid security. By making short-term forecasts of renewable energy generation power, the uncertainty of energy generation can be reduced, and it is crucial to study renewable energy forecasting techniques. This paper proposes an integrated forecasting system for renewable energy sources. Firstly, ensemble empirical mode decomposition is used for data preprocessing, and stationarity analysis is used for modal identification; then, support vector regression optimized by sparrow search algorithm and statistical methods are combined to make forecast according to different characteristics of the series respectively; finally, the feasibility of this method in renewable energy time series prediction is verified by experiments. The experiments prove that the proposed model effectively improves the accuracy and prediction performance on ultra-short-term renewable energy forecasting; and it has good applicability and competitiveness with different forecasting scenarios and characteristics, which satisfy the actual forecasting requirements in terms of operational efficiency and accuracy, thus providing a technical basis for the effective utilization of renewable energy.

Research on renewable energy prediction technology: empirical analysis for Argentina and China.

Our world needs to develop clean energy to reach the target of carbon peak and carbon neutralization. As one of clean energy, wind energy should contribute to energy conservation and emission reduction. Wind power generation is an important field of wind energy application. However, the fluctuation and intermittency of wind can affect the safety of power system. Therefore, prediction of wind power accurately for wind power safety, dispatching, and power grid development is significant. This paper proposes a prediction model of wind power, and predicts the wind power of two wind farms. For the complex wind speed series, the variational modal decomposition (VMD) method is used to reduce its volatility before prediction. And this paper presents an improved method to improve the prediction efficiency when least square support vector machine (LSSVM) predicts stationary series. The prediction result shows that the proposed model improves the prediction of wind power effectively, provides an effective method for wind farm to predict the wind power, and makes contributions to reducing carbon emissions and environmental protection.

Recent advances in the design of single-atom electrocatalysts by defect engineering.

Single-atom catalysts (SACs) with isolated metal atoms dispersed on supports have attracted increasing attention due to their maximum atomic utilization and excellent catalytic performance in various electrochemical reactions. However, SACs with a high surface-to-volume ratio are fundamentally less stable and easily agglomerate, which weakens their activity. In addition, another issue that restricts the application of SACs is the low metal loading. Defect engineering is the most effective strategy for the precise synthesis of nanomaterials to catch and immobilize single atoms through the modulation of the electronic structure and coordination environment. Herein, in this mini-review, the latest advances in designing SACs by defect engineering have been first highlighted. Then, the heteroatom doping or intrinsic defects of carbon-based support and anion vacancies or cation vacancies of metal-based supports are systematically evaluated. Subsequently, the structure-activity relationships between a single-atom coupled defect structure and electrocatalytic performance are illustrated by combining experimental results and theoretical calculations. Finally, a perspective to reveal the current challenges and opportunities for controllable preparation, in situ characterization, and commercial applications is further proposed.

Enhanced Activity of Oxygen Reduction Reaction on Pr6O11-Assisted PtPr Alloy Electrocatalysts.

Pt-based alloy catalysts for oxygen reduction reaction (ORR) with outstanding performance have been well-studied in recent years. Among these, Pt-lanthanide alloy catalysts have been developed with quite a competitive ORR activity. However, to promote practical applications of a proton-exchange membrane fuel cell (PEMFC), catalysts with superior activity are still being explored. Herein, we present the Pr6O11-assisted Pt-Pr catalyst exhibiting further improved ORR activity than the state-of-the-art Pt/C. A simple annealing treatment is applied after the synthesis of the Pt-Pr alloy, obtaining Pr6O11 nanoparticles attached to the surface of the Pt-Pr alloy to form a Pt-Pr/Pr6O11 composite catalyst. Experimental and theoretical studies reveal that the electronic state of Pt in the Pt-Pr/Pr6O11 system is modified. It was found that the strong oxophilicity of Pr adjusts the active site of Pt and promotes the adsorption and dissociation of O2. The preeminent intrinsic ORR activity on the Pt-Pr/Pr6O11 catalyst reaches the promoted specific activity (2.01 mA cm-2) and mass activity (1.3 A mgPt-1), which were 5.91- and 5.90-fold higher than those obtained by the state-of-the-art Pt/C catalyst (0.34 mA cm-2 and 0.22 A mgPt-1). This study provides us with an idea that the ORR performance of Pt-based alloy could be enhanced with the assistance of the metal oxide phase.

An innovative forecasting model to predict wind energy.

In recent years, the global wind power construction is accelerating. Although wind power is a clean energy without pollution, its volatility and irregularity have a great impact on wind power integration. Therefore, scholars pay more and more attention to the ultra-short-term prediction of wind speed. At present, the popular wind speed prediction model usually combines wind speed decomposition algorithm, machine learning algorithm, and intelligent optimization algorithm. The general wind speed decomposition algorithm cannot use the information contained in the factors affecting wind speed. Besides, the current popular optimization algorithms, such as gray wolf optimization algorithm, have strong convergence and better optimization effect, but their structure is complex and their operation complexity is large. And the PSO algorithm has simple structure and fast operation speed. To solve the above problems, a novel combination prediction model is proposed in this paper. This model uses VMD to decompose the wind speed into high-frequency signal and low-frequency signal and then uses principal component analysis and spectral clustering to extract and classify the influencing factors. In addition, aiming at the problem of slow convergence speed in the later stage of PSO iteration, an adaptive improved PSO is proposed. Finally, this paper also designs a rolling train method to adjust the size of training samples. Through four experiments of wind speed series in two periods, it is proved that the combined prediction model proposed in this paper has the following advantages: the model fully extracts the information of wind speed and influencing factors; the improved PSO algorithm has better optimization effect; rolling training method effectively improves the prediction ability of the model; the combined forecasting model has good adaptability and competitiveness.

Structure, properties of gossypol and its derivatives-from physiological activities to drug discovery and drug design.

Covering up to 2022Gossypol is a polyphenolic compound isolated from cottonseed. There are two optical enantiomers of gossypol, (-)-gossypol and (+)-gossypol. Gossypol exists as three different tautomers, aldehyde, ketone and lactol. Gossypol is toxic and provides a protective mechanism for cotton plants against pests. Gossypol was used as a male contraceptive in China in the 1970s. It was eventually abandoned due to noticeable side effects, disruption of potassium uptake and incomplete reversibility. Gossypol has gained considerable research interest due to its attractive biological activities, especially antitumor and antivirus. Gossypol derivatives are prepared by a structural modification to reduce toxicity and improve their therapeutic effect. This review depicts the bioactivity and regulation mechanisms of gossypol and its derivatives as drug lead compounds, with emphasis on its antitumor mechanism. The design and synthesis of pharmacologically active derivatives based on the structure of gossypol, such as gossypol Schiff bases, apogossypol, gossypolone, are thoroughly discussed. This review aims to serve as a reference for gossypol-based drug discovery and drug design.

Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction.

In recent years, a series of environmental problems have come one after another under the use of traditional fossil energy, such as greenhouse effect, acid rain, haze and so on. In order to solve the environmental problems and achieve sustainable development, seeking alternative resources has become the direction of joint efforts of China and the world. As an important part of new energy, wind energy needs strong wind speed prediction support in terms of providing stable electric power. As a result, it is very important to improve the accuracy of wind speed prediction. In view of this, this paper proposes a signal processing method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with singular value decomposition (SVD), and uses Elman neural network optimized by particle swarm optimization algorithm (PSO) and autoregressive integrated moving average model (ARIMA) to predict the intrinsic mode functions (IMFs). Firstly, CEEMDAN combined with SVD is used to decompose and denoise the data, and the weights and thresholds of Elman are optimized by PSO. Finally, the optimized Elman and ARIMA are used to respectively predict the processed wind speed data components, and then the final prediction results are obtained. The final prediction results show that the proposed model can improve the effect of wind speed prediction, reduce the prediction error, and provide strong support for the stable operation of wind farms and the grid connection of power plants.

Optimization scheme of wind energy prediction based on artificial intelligence.

Wind energy, as one of the renewable energies with the most potential for development, has been widely concerned by many countries. However, due to the great volatility and uncertainty of natural wind, wind power also fluctuates, seriously affecting the reliability of wind power system and bringing challenges to large-scale grid connection of wind power. Wind speed prediction is very important to ensure the safety and stability of wind power generation system. In this paper, a new wind speed prediction scheme is proposed. First, improved hybrid mode decomposition is used to decompose the wind speed data into the trend part and the fluctuation part, and the noise is decomposed twice. Then wavelet analysis is used to decompose the trend part and the fluctuation part for the third time. The decomposed data are classified. The long- and short-term memory neural network optimized by the improved particle swarm optimization algorithm is used to train the nonlinear sequence and noise sequence, and the autoregressive moving average model is used to train the linear sequence. Finally, the final prediction results were reconstructed. This paper uses this system to predict the wind speed data of China's Changma wind farm and Spain's Sotavento wind farm. By experimenting with the real data from two different wind farms and comparing with other predictive models, we found that (1) by improving the mode number selection in the variational mode decomposition, the characteristics of wind speed data can be better extracted. (2) According to the different characteristics of component data, the combination method is selected to predict modal components, which makes full use of the advantages of different algorithms and has good prediction effect. (3) The optimization algorithm is used to optimize the neural network, which solves the problem of parameter setting when establishing the prediction model. (4) The combination forecasting model proposed in this paper has clear structure and accurate prediction results. The research work in this paper will help to promote the development of wind energy prediction field, help wind farms formulate wind power regulation strategies, and further promote the construction of green energy structure.